vkhuc

5 tahun yang laludiscuss

Absolutely. This dataset search was first introduced in September of 2018. It was out of beta in January last year: https://www.kdnuggets.com/2020/01/google-dataset-search.html.

vkhuc

11 tahun yang laludiscuss

Thanks. That's the book I'm reading :)

vkhuc

11 tahun yang laludiscuss

Although I'm working on deep neural nets, this material is too advanced to me. Looks like deep nets + bayesian reasoning is the next big thing.

vkhuc

11 tahun yang laludiscuss

Why use NLTK when we can use spaCy instead? http://spacy.io/

vkhuc

11 tahun yang laludiscuss

Nice. As a guy who got involved into OpenNLP's development, I'd like to see the comparison too.

Also, it would be great if you include SENNA into your benchmark: http://ml.nec-labs.com/senna/

vkhuc

12 tahun yang laludiscuss

I think they used the methods described in http://www.cs.berkeley.edu/~rbg/papers/r-cnn-cvpr.pdf

vkhuc

12 tahun yang laludiscuss

I've been looking at Factorie for a while but haven't actually done anything heavy with it.

I planned to replace the optimizer in CMU's POS tagger with the one implemented in OpenNLP to make the tagger fully Apache. Unfortunately, so busy right now. Currently, I'm running the tagger on AWS, so the GPL doesn't hurt me much.

BTW, besides the POS tagger, CMU's TweeboParser depends on Turbo Parser which again is licensed under GPL.

vkhuc

12 tahun yang laludiscuss

Yes, Noah Smith's NLP group at CMU is awesome. Btw, Noah is moving to UWash.

vkhuc

12 tahun yang laludiscuss

The whole project is based on various libraries. In particular, the POS tagger itself uses the OWLQN optimizer from Stanford NLP (licensed under GPL).

However, it's possible to remove GPL libraries out of the POS tagger as mentioned here: https://github.com/brendano/ark-tweet-nlp/blob/master/LICENS...

vkhuc

12 tahun yang laludiscuss

Great, I've been waiting for the release of TweeboParser. Just before EMNLP14 gets started.

vkhuc

12 tahun yang laludiscuss

There are some (free) good books that haven't been mentioned yet:

1) "Data Mining and Analysis: Fundamental Concepts and Algorithms" by Zaki and Meira http://www.cs.rpi.edu/~zaki/PaperDir/DMABOOK.pdf

This book covers many ML topics with concrete examples.

2) "Computer Vision: Models, Learning, and Inference" by Simon Prince: http://web4.cs.ucl.ac.uk/staff/s.prince/book/book.pdf

Despite a CV book, the first half of it is like a statistics book that comes with examples in CV which are very easy to follow.

vkhuc

12 tahun yang laludiscuss

I skimmed over Mohri's book and I think the topics it covers are quite narrow.

For mathematical foundations of ML, I would recommend the book "Understanding Machine Learning: From Theory to Algorithms" by Shai Shalev-Shwartz.

A brief version of the book is available to download on the author's website: http://www.cs.huji.ac.il/~shais/Handouts.pdf

vkhuc

12 tahun yang laludiscuss

If you prefer C, you can try SENNA: http://ml.nec-labs.com/senna/. It includes POS tagging, chunking, NER, constituency parsing and semantic role labeling. But dependency parsing is not there yet.

It's super fast (thanks to C) and very accurate (thanks to Deep Learning approach). The license is not for commercial usage though.

SENNA can be used with NLTK: http://pydoc.net/Python/nltk/2.0.2/nltk.tag.senna/

vkhuc

12 tahun yang laludiscuss

Besides Julia, I think another alternate language to Python for scientific computation would be Scala. Breeze (from ScalaNLP project) is an effort to bring Numpy and Matlab syntax to Scala: https://github.com/scalanlp/breeze/wiki/Breeze-Linear-Algebr...

The learning curve for Scala may be steep though.

vkhuc

12 tahun yang laludiscuss

I think the book "Speech and Language Processing, 2nd Edition" from Prof. Daniel Jurafsky is very good http://www.amazon.com/Speech-Language-Processing-Daniel-Jura...

You can also check out the great online NLP course taught by the author and Prof. Chris Manning from Stanford: https://www.youtube.com/watch?v=nfoudtpBV68&list=PL6397E4B26...

Submissions

Stanford Alpaca: An Instruction-following LLaMA model

GPT-3's Response to Philosophers

Announcing the Stack Exchange Time Machine

DenseCap: Fully Convolutional Localization Networks for Dense Captioning

Google opens access to its speech recognition API

End-To-End Memory Networks for question-answering tasks with demo

Yann LeCun Keynote – Obstacles on the Path to AI

What a Deep Neural Network thinks about selfies

Big Data Doesn't Exist

Question answering on the Facebook bAbi dataset using RNN

Live Cyber Attacks Around the World

Nvidia's demo of real-time object recognition using deep learning [video]

BMW Shows Off Gesture Controls and Automated Parking

New Startup Sets Out to Bring Google-Style AI to the Masses

MetaMind – AI for Enterprise

An interactive tutorial on non-parametric Bayes

Breaking Bin Laden: Visualizing the Power of a Single Tweet

Glove: Global vectors for word representation

Deep Learning, NLP, and Representations

Does Deep Learning Have Deep Flaws?

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